Cognitive Roaming Support

ABSTRACT

Methods and systems are described for generating and utilizing a pattern of association. The pattern of association can comprise information that indicates to which of a plurality of network devices one or more mobile devices are likely to connect. The pattern of association can comprise information that indicates an order of association. The pattern of association can be associated with one or more factors which can be any information that provides insight into the pattern of association. The pattern of association can be used to identify a next network device that a mobile device will transition to based on which network device the mobile device is currently connected to. Data, such as authentication information, can be pushed to the identified next network device to reduce network connectivity issues that may occur by transitioning between network devices.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/726,153, filed Oct. 5, 2017, which is a continuation of U.S.application Ser. No. 15/048,451, filed Feb. 19, 2016, issued as U.S.Pat. No. 9,826,388, both of which are herein incorporated by referencein their entirety

BACKGROUND

Mobile devices typically access a network via a network device such as anetwork device (e.g., access point). In the event the mobile devicemoves outside of the range of the network device, the mobile device willhave to connect to a different network device to continue to access thenetwork. Transitioning between network devices can be time and resourceintensive. For example, a mobile device may need to undergo anauthentication process when transitioning to a new network device. Theauthentication process can require memory allocation, computationalprocesses, and reservation of a channel, all of which can delay orotherwise interfere with a user experience. What is needed is a way toseamlessly transition between network devices so that the transition isless perceptible to a user. These and other shortcomings are addressedby the present disclosure.

SUMMARY

It is to be understood that both the following general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive. The present disclosure relates to systems andmethods for predicting the next network device (e.g., access point) awireless device (e.g., mobile device) will try to connect with. As amobile device changes from connecting from one network device to anothernetwork device, movement (e.g., space-time path, a history of networkdevices) of the mobile device can be determined (e.g., tracked). Apattern of association can be created based on the movement of themobile device. The pattern of association can comprise information thatindicates which of the plurality of network devices the mobile devicesare likely to connect to and in what order. The pattern of associationcan comprise percentages and/or rankings that indicate the likelihoodthat the mobile device will associate with one or more network devices.

The pattern of association can be based on movements of an individualwireless device (e.g., mobile device), groups of wireless devices (e.g.,mobile devices), an individual network device, and/or groups of networkdevices.

The pattern of association can be used to predict which network devicethe wireless device (e.g., mobile device) might seek to associate withnext. The pattern of association can also be associated with one or morefactors. A factor can comprise a circumstance (e.g., environmentalinformation, calendar information, timing information), activity (e.g.,application in use, type of movement), characteristic (e.g., usercharacteristic, device characteristic), and/or the like. Factors can berelated to and/or associated with the pattern of association. Thefactors can be any information that provides insight into the pattern ofassociation. Example factors include, but are not limited to, a day ofthe week, a time of a day, a weather condition, an event, and the like.

Upon detection of the wireless device (e.g., mobile device) at a firstnetwork device, the pattern of association can be used to determine asecond network device that the mobile device is likely to connect tonext. Information related to the mobile device can be provided to thesecond network device in anticipation of a future connection. Thisinformation can allow seamless association with the mobile device whenit enters range of the second network device. For example,authentication information, such as keys, can be provided to the secondnetwork device. The first network device can transmit one or more keysused by the mobile device to access the first network device to thesecond network device. In another aspect, the one or more keys can betransmitted to the second network device from a remote computing devicesuch as a server. The second network device can pre-authenticate themobile device based on the one or more keys. In another aspect, thepattern of association can be utilized to direct delivery of data from aremote server. For example, data currently consumed by the mobile devicecan be multicast to the first network device and the second networkdevice. The second network device can deliver the data to the mobiledevice once the mobile device is in range, so that the data consumptionby the mobile device is seamless as the mobile device transitionsbetween the first network device and the second network device.

Additional advantages will be set forth in part in the description whichfollows or may be learned by practice. The advantages will be realizedand attained by means of the elements and combinations particularlypointed out in the appended claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments and together with thedescription, serve to explain the principles of the methods and systems:

FIG. 1 is a block diagram of an exemplary system and network;

FIG. 2 is a block diagram illustrating an exemplary path;

FIG. 3 is an example of a pattern of association;

FIG. 4 is a flow chart of an exemplary method;

FIG. 5 is a flow chart of an exemplary method;

FIG. 6 is a flow chart of an exemplary method; and

FIG. 7 is a block diagram of an exemplary computing device.

DETAILED DESCRIPTION

Before the present methods and systems are disclosed and described, itis to be understood that the methods and systems are not limited tospecific methods, specific components, or to particular implementations.It is also to be understood that the terminology used herein is for thepurpose of describing particular embodiments only and is not intended tobe limiting.

As used in the specification and the appended claims, the singular forms“a,” “an,” and “the” include plural referents unless the context clearlydictates otherwise. Ranges may be expressed herein as from “about” oneparticular value, and/or to “about” another particular value. When sucha range is expressed, another embodiment includes from the oneparticular value and/or to the other particular value. Similarly, whenvalues are expressed as approximations, by use of the antecedent“about,” it will be understood that the particular value forms anotherembodiment. It will be further understood that the endpoints of each ofthe ranges are significant both in relation to the other endpoint, andindependently of the other endpoint.

“Optional” or “optionally” means that the subsequently described eventor circumstance may or may not occur, and that the description includesinstances where said event or circumstance occurs and instances where itdoes not.

Throughout the description and claims of this specification, the word“comprise” and variations of the word, such as “comprising” and“comprises,” means “including but not limited to,” and is not intendedto exclude, for example, other components, integers or steps.“Exemplary” means “an example of” and is not intended to convey anindication of a preferred or ideal embodiment. “Such as” is not used ina restrictive sense, but for explanatory purposes.

Disclosed are components that can be used to perform the disclosedmethods and systems. These and other components are disclosed herein,and it is understood that when combinations, subsets, interactions,groups, etc. of these components are disclosed that while specificreference of each various individual and collective combinations andpermutation of these may not be explicitly disclosed, each isspecifically contemplated and described herein, for all methods andsystems. This applies to all aspects of this application including, butnot limited to, steps in disclosed methods. Thus, if there are a varietyof additional steps that can be performed it is understood that each ofthese additional steps can be performed with any specific embodiment orcombination of embodiments of the disclosed methods.

The present methods and systems may be understood more readily byreference to the following detailed description of preferred embodimentsand the examples included therein and to the Figures and their previousand following description.

As will be appreciated by one skilled in the art, the methods andsystems may take the form of an entirely hardware embodiment, anentirely software embodiment, or an embodiment combining software andhardware aspects. Furthermore, the methods and systems may take the formof a computer program product on a computer-readable storage mediumhaving computer-readable program instructions (e.g., computer software)embodied in the storage medium. More particularly, the present methodsand systems may take the form of web-implemented computer software. Anysuitable computer-readable storage medium may be utilized including harddisks, CD-ROMs, optical storage devices, or magnetic storage devices.

Embodiments of the methods and systems are described below withreference to block diagrams and flowchart illustrations of methods,systems, apparatuses and computer program products. It will beunderstood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, respectively, can be implemented by computerprogram instructions. These computer program instructions may be loadedonto a general purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions which execute on the computer or other programmabledata processing apparatus create a means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture such as computer-readableinstructions for implementing the function specified in the flowchartblock or blocks. The computer program instructions may also be loadedonto a computer or other programmable data processing apparatus to causea series of operational steps to be performed on the computer or otherprogrammable apparatus to produce a computer-implemented process suchthat the instructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, blocks of the block diagrams and flowchart illustrationssupport combinations of means for performing the specified functions,combinations of steps for performing the specified functions and programinstruction means for performing the specified functions. It will alsobe understood that each block of the block diagrams and flowchartillustrations, and combinations of blocks in the block diagrams andflowchart illustrations, can be implemented by special purposehardware-based computer systems that perform the specified functions orsteps, or combinations of special purpose hardware and computerinstructions.

The present disclosure relates to systems and methods for predictingwhich network device (e.g., access point) a mobile device will try toconnect with next. The mobile device can associate (e.g., connect,communicate) with a current network device by utilizing one or more keysin order to send/receive information. For example, the mobile device canreceive content (e.g., a website, streaming media, an e-book, etc.) viathe current network device.

In an aspect, an identifier of the current network device and/or a nextnetwork device can be stored in a remote server and/or in one or morenetwork devices (e.g., access points). For example, the identifier canbe stored along with identifiers of other neighboring network devices.Association (e.g., connection, communication) of the mobile device withthe current network device can be a strong indication of the nextnetwork device. For example, a network device at a first train stop mayhave five neighboring network devices. However, the vast majority ofmobile devices that associate with the network device at the first trainstop will likely next associate with either a network device at the nexttrain stop north or a network device at the next train stop south. In anaspect, association with successive network devices (e.g., multiplenetwork devices along a path or direction) can be a strong indication ofthe next network device. For example, the vast majority of mobiledevices that associate with the network device at the first train stopand were recently previously associated with the network device at theadjacent train stop north will likely associate next with the next trainstop south.

In an aspect, as mobile devices traverse paths, one or more patterns ofassociation can be created based on the movement of the mobile devices.The pattern(s) of association can be created for an individual mobiledevice, groups of mobile devices, an individual network device, and/orgroups of network devices. The pattern(s) of association thus createdcan be used to predict which of the one or more network devices themobile device(s) might seek to associate with next. The pattern(s) ofassociation can be associated with one or more factors. The one or morefactors can be any information that provides insight into the pattern ofassociation. In an aspect, the pattern of association can berepresented, stored, and/or the like as a data structure. The datastructure representing the pattern of association can comprise one ormore identifiers, such as identifiers of the current network device(e.g., access point), the mobile device associated with the currentnetwork device, the next network device, neighboring network devices.The data structure representing the pattern of association can furthercomprise data indicative of the one or more factors.

Once created, the one or more patterns of association can be associatedwith the current network device (e.g., access point) and/or one or moreof the mobile devices. For example, the one or more patterns ofassociation can indicate that one or more mobile devices associated withthe current network device will likely next associate with a secondnetwork device. The current network device can transmit the one or morekeys used by the one or more mobile devices to access the currentnetwork device to the second network device. In another aspect, the oneor more keys can be transmitted to the second network device from aremote computing device such as a server. The second network device canuse the one or more keys to pre-authenticate the one or more mobiledevices, so that the one or more mobile devices can seamlessly associatewith the second network device once the one or more mobile devices arein range.

In an aspect, data delivered from a remote server to the mobile devicecan be multicast to the current network device and the second networkdevice. The second network device can deliver the data to the mobiledevice once the mobile device is in range, so that the transitionbetween the current network device and the second network device isrelatively seamless.

In one aspect of the disclosure, illustrated in FIG. 1, a system can beconfigured to provide services such as network-related services to auser device. In an aspect, a path, such as a walkway (e.g., at a mall oran amusement park) or a transportation route (e.g., train route, roadway), can comprise multiple network devices. In another aspect, areas(e.g., geographic areas, buildings, structures), such as city blocks,office buildings, parks, or sports arenas, can comprise multiple networkdevices. In an aspect, associations (e.g., connections, communications,etc. . . . ) can be made between the multiple network devices (e.g.,access points) and one or more user devices (e.g., mobile device,tablet, smart phone, etc.). Those skilled in the art will appreciatethat present methods may be used in various types of networks andsystems that employ both digital and analog equipment. One skilled inthe art will appreciate that provided herein is a functional descriptionand that the respective functions can be performed by software,hardware, or a combination of software and hardware.

FIG. 1 illustrates various aspects of an exemplary environment in whichthe present methods and systems can operate. The network and system cancomprise a user device 102 in communication with a computing device 104such as a server, for example. The computing device 104 can be disposedlocally or remotely relative to the user device 102. As an example, theuser device 102 and the computing device 104 can be in communication viaa private and/or public network 105 such as the Internet or a local areanetwork. Other forms of communications can be used such as wired andwireless telecommunication channels, for example.

In an aspect, the user device 102 can be an electronic device such as acomputer, a smartphone, a laptop, a tablet, a set top box, a displaydevice, or other device capable of communicating with the computingdevice 104. As an example, the user device 102 can comprise acommunication element 106 for providing an interface to a user tointeract with the user device 102 and/or the computing device 104. Thecommunication element 106 can be any interface for presenting and/orreceiving information to/from the user, such as user feedback. Anexample interface may be communication interface such as a web browser(e.g., Internet Explorer °, Mozilla Firefox, Google Chrome®, Safari orthe like). Other software, hardware, and/or interfaces can be used toprovide communication between the user and one or more of the userdevice 102 and the computing device 104. As an example, thecommunication element 106 can request or query various files from alocal source and/or a remote source. As a further example, thecommunication element 106 can transmit data to a local or remote devicesuch as the computing device 104.

In an aspect, the user device 102 can be associated with a useridentifier or a device identifier 108. As an example, the deviceidentifier 108 can be any identifier, token, character, string, or thelike, for differentiating one user or user device (e.g., user device102) from another user or user device. In a further aspect, the deviceidentifier 108 can identify a user or user device as belonging to aparticular class of users or user devices. As a further example, thedevice identifier 108 can comprise information relating to the userdevice 102 such as a manufacturer, a model or type of device, a serviceprovider associated with the user device 102, a state of the user device102, a locator, and/or a label or classifier. Other information can berepresented by the device identifier 108.

In an aspect, the device identifier 108 can comprise an address element110 and a service element 112. In an aspect, the address element 110 cancomprise or provide an internet protocol address, a network address, amedia access control (MAC) address, an Internet address, or the like. Asan example, the address element 110 can be relied upon to establish acommunication session between the user device 102 and the computingdevice 104 or other devices and/or networks. As a further example, theaddress element 110 can be used as an identifier or locator of the userdevice 102. In an aspect, the address element 110 can be persistent fora particular network.

In an aspect, the service element 112 can comprise an identification ofa service provider associated with the user device 102 and/or with theclass of user device 102. The class of the user device 102 can berelated to a type of device, capability of device, type of service beingprovided, and/or a level of service (e.g., business class, service tier,service package, etc.). As an example, the service element 112 cancomprise information relating to or provided by a communication serviceprovider (e.g., Internet service provider) that is providing or enablingdata flow such as communication services to the user device 102. As afurther example, the service element 112 can comprise informationrelating to a preferred service provider for one or more particularservices relating to the user device 102. In an aspect, the addresselement 110 can be used to identify or retrieve data from the serviceelement 112, or vice versa. As a further example, one or more of theaddress element 110 and the service element 112 can be stored remotelyfrom the user device 102 and retrieved by one or more devices such asthe user device 102 and the computing device 104. Other information canbe represented by the service element 112.

In an aspect, the computing device 104 can be a server for communicatingwith the user device 102. As an example, the computing device 104 cancommunicate with the user device 102 for providing data and/or services.As an example, the computing device 104 can provide services, such asnetwork (e.g., Internet) connectivity, network printing, mediamanagement (e.g., media server), content services, streaming services,broadband services, or other network-related services. In an aspect, thecomputing device 104 can allow the user device 102 to interact withremote resources, such as data, devices, and files. As an example, thecomputing device can be configured as (or disposed at) a centrallocation (e.g., a headend, or processing facility), which can receivecontent (e.g., data, input programming) from multiple sources. Thecomputing device 104 can combine the content from the multiple sourcesand can distribute the content to user (e.g., subscriber) locations viaa distribution system.

In an aspect, the computing device 104 can manage the communicationbetween the user device 102 and a database 114 for sending and receivingdata therebetween. As an example, the database 114 can store a pluralityof files (e.g., web pages), user identifiers or records, or otherinformation. As a further example, the user device 102 can requestand/or retrieve a file from the database 114. In an aspect, the database114 can store information relating to the user device 102 such as theaddress element 110 and/or the service element 112. As an example, thecomputing device 104 can obtain the device identifier 108 from the userdevice 102 and retrieve information from the database 114 such as theaddress element 110 and/or the service elements 112. As a furtherexample, the computing device 104 can obtain the address element 110from the user device 102 and can retrieve the service element 112 fromthe database 114, or vice versa. Any information can be stored in andretrieved from the database 114. The database 114 can be disposedremotely from the computing device 104 and accessed via direct orindirect connection. The database 114 can be integrated with thecomputing system 104 or some other device or system.

In an aspect, one or more network devices 116 a,b,c,d can be incommunication with a network, such as the network 105. As an example,one or more of the network devices 116 a,b,c,d can facilitate theconnection of a device, such as user device 102, to the network 105. Asa further example, one or more of the network devices 116 a,b,c,d can beconfigured as a wireless access point (WAP). In an aspect, one or moreof the network devices 116 a,b,c,d can be configured to allow one ormore wireless devices to connect to a wired and/or wireless networkusing Wi-Fi, Bluetooth Zigbee®, or any desired method or standard.

In an aspect, one or more of the network devices 116 a,b,c,d can beconfigured as a local area network (LAN). As an example, one or more ofthe network devices 116 a,b,c,d can comprise a dual band wireless accesspoint. As an example, one or more of the network devices 116 a,b,c,d canbe configured with a first service set identifier (SSID) (e.g.,associated with a user network or private network) to function as alocal network for a particular user or users. As a further example, oneor more of the network devices 116 a,b,c,d can be configured with asecond service set identifier (SSID) (e.g., associated with apublic/community network or a hidden network) to function as a secondarynetwork or redundant network for connected communication devices.

One or more of the network devices 116 a,b,c,d can comprise anidentifier 118 a,b,c,d. As an example, one or more identifiers can be orrelate to an Internet Protocol (IP) Address (e.g., IPV4/IPV6) or a mediaaccess control address (MAC address) or the like. As a further example,one or more of the identifiers 118 a,b,c,d can be a unique identifierfor facilitating communications on the physical network segment. In anaspect, each of the network devices 116 a,b,c,d can comprise a distinctidentifier 118 a,b,c,d. As an example, the identifiers 118 a,b,c,d canbe associated with a physical location of the network devices 116a,b,c,d.

In an aspect, the user device 102 can currently be associated with oneof the network devices 116 a,b,c,d, such as the network device 116 b. Inan aspect, the currently associated network device 116 b can compriseone or more keys 120 to facilitate association with the user device 102.In another aspect, the one or more keys 120 can be stored in one or moreof the user device 102, the currently associated network device 116 b,and/or the computing device 104. In an aspect, the currently associatednetwork device 116 b and/or the computing device 104 can comprisecontent 122 to push to the user device 102.

In an aspect, the computing device 104 and/or the one or more networkdevices 116 a,b,c,d can monitor associations (e.g., connections,communications) between the one or more network devices 116 a,b,c,d andone or more user devices 102. In an aspect, the computing device 104and/or the one or more network devices 116 a,b,c,d can create and storepatterns of association that identify which of the one or more userdevices 102 was associated with which of the one or more network devices116 a,b,c,d and in what order. The resulting patterns of associationidentify an order in which a user device 102 (or multiple user devices102) is likely to connect to the one or more network devices 116a,b,c,d.

Association of a user device (e.g., mobile device) with one of thenetwork devices 116 a,b,c,d can be a strong indicator that the mobiledevice will next associate with another of the network devices 116a,b,c,d based on the physical location of the network devices 116a,b,c,d and knowledge of the physical location(s) of the network devices116 a,b,c,d in the vicinity. For example, if a mobile device isassociated with a network device (e.g., access point) at a first trainstop (e.g., 5^(th) Street train stop), then a prediction can be madethat the mobile device will next associate with a network device at asecond train stop (e.g., 6^(th) Street train stop). If the mobile devicenext associates with the network device at the second train stop, then aprediction can be made that the mobile device will next associate with anetwork device at a third train stop (e.g., 7^(th) Street train stop).In such a situation, the pattern of association can be based on theknowledge of the physical location of the train stops.

One or more patterns of association 124 can be determined, generated,created, and/or the like for an individual user device 102 and/or forgroups of user devices 102. The pattern(s) of association 124 canpredict which of the one or more network devices 116 a,b,c,d the userdevice 102 might seek to associate with next based on a pattern ofassociation 124 specific to the user device 102 and/or a general patternof association 124 applicable to a group of user devices 102. Thepattern of association 124 can comprise percentages and/or rankings thatindicate the likelihood that the user device 102 will associate with oneor more network devices 116 a,b,c,d.

The patterns of association 124 can be associated with one or morefactors. The factors can be any information that provides insight intothe pattern of association. A factor can comprise a circumstance (e.g.,environmental information, calendar information, timing information),activity (e.g., application in use, type of movement), characteristic(e.g., user characteristic, device characteristic), and/or the like. Inan aspect, factors can be associated with an environment. In an aspect,factors associated with an environment can be associated with anenvironment in which the phone is operating. In an aspect, theenvironment in which the phone is operating can comprise a physicalenvironment. In an aspect, the environment in which the phone isoperating can comprise a digital environment. In an aspect, factorsassociated with an environment can comprise a circumstance (e.g.,environmental information, calendar information, timing information),activity (e.g., application in use, type of movement), characteristic(e.g., user characteristic, device characteristic), and/or the like.Factors can be related to and/or associated with patterns of associationfor a specific user device or network device and/or with patterns ofassociation for a group of mobile devices and/or network devices, and/orthe like. Factors that are related to a general pattern of associationcan comprise, for example, a time of day, a day of the week, a timerelative to an event (e.g., two minutes until halftime), a time relativeto a schedule (e.g., a transportation vehicle is scheduled to arrive infour minutes), types of applications executing on the user devices 102,weather (e.g., a path and/or a pace taken by the user devices 102 whenit is raining and/or cold outside), etc. Patterns of association thatare related to a specific user device 102 can also comprise otherfactors specific to the user device 102, such as a time the user device102 has been associated with one or more network devices 116 a,b,c,d, acomprehensive history of one or more network devices 116 a,b,c,d withwhich the user device 102 has associated, an abbreviated history of oneor more network devices 116 a,b,c,d with which the user device 102 hasassociated (e.g., the current trip, etc.), a history in relation toother factors (e.g., the path the user device 102 normally takes onTuesday afternoons, etc.), a location (e.g., the path the user device102 normally takes when in this city, etc.), types of applicationsexecuting on the user device 102 (e.g., a home security application, ahome automation application, and the like), weather (e.g., a path and/ora pace the user device 102 takes when it is raining and/or coldoutside), etc.

Factors can be determined through the use of sensors, received signals,user input, services (such as, for example, a weather service, an eventscheduling service, etc.), and/or any other suitable method. Forexample, a thermometer can be used to determine a current temperature atthe one or more network devices 116 a,b,c,d. By way of further example,a local and/or remote clock can be used to determine a current timeand/or date that the user device 102 associated with the one or morenetwork devices 116 a,b,c,d. Data indicative of the factors can bestored in association with the patterns of association 124 in order tosupplement the accuracy of the patterns of association 124.

In an aspect, the computing device 104 and/or the one or more networkdevices 116 a,b,c,d can store the patterns of association 124 andassociate the patterns of association 124 with the device identifier 108of the user device 102 that was the basis for, resulted in, and/orcontributed to the creation of the patterns of association 124. In thatway, the computing device 104 and/or the one or more network devices 116a,b,c,d can identify which of many stored patterns of association 124are applicable to the user device 102.

For example, as shown in FIG. 1, the network device 116 b can associate(e.g., connect, communicate) with the user device 102. The networkdevice 116 b can receive the device identifier 108 as part ofassociating with the user device 102. The network device 116 b can usethe device identifier 108 to determine if there are any patterns ofassociation 124 that apply to the user device 102 or a subset of userdevices to which the user device 102 belongs. In an aspect, the networkdevice 116 b can determine that a plurality of patterns of association124 is associated with the user device 102. The network device 116 b candetermine the existence/condition of one or more factors that canindicate that one pattern of association 124 of the plurality ofpatterns of association 124 should be used over another pattern ofassociation 124. For example, if the current day is Tuesday, then thepattern of association 124 retrieved can be for Tuesdays or weekdays. Ina further example, if the weather conditions are sunny and 80 degrees,then the pattern of association 124 retrieved can be for a sunny day orabove and/or below certain temperature thresholds (e.g., above 50degrees but below 95 degrees). The pattern of association 124 retrievedcan predict one or more likely next network devices.

For example, the network device 116 b can determine that there is afirst pattern of association 124 associated with the user device 102that is associated with a factor of 7:00 am to 9:00 am and that there isa second pattern of association 124 associated with the user device 102that is associated with a factor of 4:00 pm to 7:00 pm. The networkdevice 116 b can determine if the current time is between 7:00 am to9:00 am, 4:00 pm to 7:00 pm, or neither. If the current time isdetermined to be between 7:00 am to 9:00 am, then the network device 116b can utilize the first pattern of association 124. If the current timeis determined to be between 4:00 pm to 7:00 pm, then the network device116 b can utilize the second pattern of association 124 (the secondpattern of association 124 is illustrated in FIG. 3). If the currenttime is determined to not be between 7:00 am to 9:00 am or 4:00 pm to7:00 pm, then the network device 116 b can utilize no pattern ofassociation 124, but such association with the network device 116 b canbe monitored to assist in the creation of a new pattern of association124. The network device 116 b can utilize the second pattern ofassociation 124 to determine that the next network device that the userdevice 102 is likely to associate with next is network device 116 cbased on the pattern of association 124 and can transmit the keys 120and/or content 122 to the network device 116 c.

In an aspect, one or more keys 120 can be pushed (e.g., communicated)between the network devices 116 a,b,c,d and/or pushed to the networkdevices 116 a,b,c,d from the computing device 104. In an aspect, the oneor more keys 120 can be used to pre-authenticate the user device 102 atthe one or more likely next network devices 116 a,b,c,d before the userdevice 102 gets in range of one of the one or more likely next networkdevices 116 a,b,c,d. The one or more keys 120 can be associated with atime-to-live (TTL) after which the one or more keys 120 can be discardedby the network devices 116 a,b,c,d. If the user device 102 in factassociated with the likely next network device 116 a,b,c,d, theassociation can be logged and reported to the computing device 104. Inan aspect, such an association can be used to increase a weight given tothe pattern of association, wherein a higher weight indicates higherreliability of the pattern of association. If the user device 102 didnot in fact associate with the likely next network device 116 a,b,c,d,the failure to associate can be logged and reported to the computingdevice 104. In an aspect, such a failed association can be used todecrease the weight given to the pattern of association, wherein a lowerweight indicates low reliability of the pattern of association.

Content 122 can be pushed (e.g., provided, communicated, sent) to theone or more likely next network devices 116 a,b,c,d. Static content,such as a website or e-book, can simply be pushed to the one or morelikely next network devices 116 a,b,c,d. In an aspect, in the case ofstreaming content, content delivered to the current network device 116a,b,c,d can also be delivered to the one or more likely next networkdevices 116 a,b,c,d (e.g., the streaming content can be multicast to thecurrent network device and the one or more likely next network devices).In an aspect, in the case of streaming content, an estimation can bemade about the time of arrival at one or more likely next networkdevices 116 a,b,c,d. In an aspect, an approximation of where in thecontent (e.g., a timestamp, a content fragment, and the like) the userdevice 102 will be on arrival at one or more likely next network devicescan be made based on the estimation. In an aspect, a portion of thecontent can be pushed to the one or more likely next network devices andheld in storage based on the approximation. For example, if it isestimated that the mobile device will be at a likely next network devicein 2 minutes, and the mobile device is currently 17 minutes into thecontent, then the approximation can be that the mobile device will be at19 minutes when the mobile device arrives in range of the likely nextnetwork device. A 30-second portion of the content starting at 18minutes and 45 seconds into the content and ending at 19 minutes and 15seconds into the content can be pushed to a buffer of the likely nextnetwork device.

Thus the patterns of association 124 can be used to preposition (e.g.,provide/transmit in advance) keys 120 and/or content 122 at a nextnetwork device to ensure a smooth transition of content and/or dataconsumption by the user device 102 at the next network device.

FIG. 2 illustrates a path 214 (dashed line) taken by a user device 102through a group of network devices that can result in creation of apattern of association. The pattern of association can be based on anobserved history of associations of the user device 102 and/or a historyof associations for user devices (e.g., group of user devices) ingeneral. In an aspect, the path 214 can begin with a user device 102connected to a network device 202 associated with a train stationparking lot. In an aspect, next on the path 214, the user device 102 canconnect to a network device 204 associated with a train station stopnear the train station parking lot. Next on the path 214, the userdevice 102 can connect to a network device 206 associated with adestination train station stop. At an end of the path 214, the userdevice 102 can connect to a network device 210 associated with anoffice. In an aspect, a pattern of association can be created based onthe associations made by the user device 102 and used to determine anext network device.

In an aspect, the next time the user device 102 is connected to thenetwork device 202, the pattern of association 124 can be accessed todetermine that the network device 204 is the likely next network device.The one or more keys 120 and/or content 122 can then be pushed to thenetwork device 204 (e.g., in advance of the user device 102 associatingwith the network device 204). In an aspect, the one or more keys 120and/or content 122 can be pushed from network device to network deviceor from a remote computing device to the various network devices.Similarly, the pattern of association 124 can be stored and accessedlocally on each network device or remotely from a computing device. Inan aspect, when the user device 102 is connected to the network device204, the pattern of association 124 can be accessed to determine thatthe network device 206 is the likely next network device. The one ormore keys 120 and/or content 122 can then be pushed to the networkdevice 206 in advance. In an aspect, when the user device 102 isconnected to the network device 206, the pattern of association 124 canbe accessed to determine that the network device 210 is the likely nextnetwork device. The one or more keys 120 and/or content 122 can then bepushed to the network device 210. In an aspect, even though a networkdevice 208 or a network device 212 may be closer to the network device206, the pattern of association 124 can reveal that it is more likelythat the user device 102 will next connect with the network device 210.In a further aspect, the one or more keys 120 and/or content 122 can besimultaneously pushed to the network devices 204, 206, and 210 once thepattern of association 124 is accessed by the network device 202.

FIG. 3 illustrates an exemplary representation of a pattern ofassociation (e.g., migration) 124 supplemented with one or more factors310. The pattern of association can be for a mobile device currentlyassociated with a particular network device. For example, the pattern ofFIG. 3 is for mobile devices currently associated with the networkdevice 116 b. The pattern of association in FIG. 3 can comprise factors,such as the day of the week (e.g., Tuesday), the length of time that theuser device has been associated with the current network device (e.g.,more than 15 minutes), and a current time frame (e.g., 4:00-7:00 PM). Inan aspect, the likelihood of association can be assigned to one or moreof the network devices 116 a,b,c,d. In an aspect, the likelihood ofassociation can be a ranking, wherein one or more network devices areranked in order of likelihood of next association. In an aspect, asshown in FIG. 3, the likelihood of association can comprise apercentage. Percentages can be determined by applying standardstatistical methods to patterns of association. For example, byaggregating a total next association denominator and calculating a nextassociation numerator for each network device that is a candidate for“next association.” In an aspect, the percentage can represent aprobability that a user device will associate with a correspondingnetwork device (e.g., next, within a time period). In an aspect, thecurrently associated network device may or may not be considered as alikely next network device in the pattern. In an aspect, the percentagecan represent a probability that a user device will be associated with acorresponding network device in a time period. For example, thepercentages in FIG. 3 can represent the probability that a user devicewill be associated with the corresponding network device (e.g., next, inthe next 5 minutes). The pattern of association of FIG. 3 is illustratedas a plurality of arrows originating from the network device 116 b andpointing to one of the network devices 116 a,b,c,d. Each arrow has acorresponding percentage representing the likelihood of association ofthe network device being pointed to. In an aspect, one of thepercentages can represent a probability that the mobile device will beconnected to the currently associated network device, as is shown inFIG. 3 (45%). In an aspect, the pattern can only consider theprobabilities that the mobile device will be connected to networkdevices other than the currently associated network device. In anaspect, when a likelihood of association comprises a ranking, thelikelihood of association can be represented as a list.

In an aspect, one or more likely next network devices can be chosen forthe mobile device. In an aspect, the top x number of most likely nextnetwork devices can be chosen. In an aspect, if a probability exceeds athreshold percentage, then it can be chosen as a likely next networkdevice. For example, if the percentage that a mobile device will move toa particular network device exceeds 35%, then the particular networkdevice can be chosen as a likely next network device. In an aspect, ifthe currently associated network device only has one neighboring networkdevice, then the neighboring network device can be chosen as a likelynext network device without considering rankings or percentages. Inanother aspect, if the currently associated network device has less thana threshold number of neighboring network devices, then all of theneighboring network devices can be chosen as likely next networkdevices. In FIG. 3, the network device 116 c can be chosen as a likelynext network device because the network device 116 c has a higherpercentage than the other next network device candidates (the networkdevice 116 a and the network device 116 d). In an aspect, the networkdevice 116 b and/or the computing device 104 can choose the likely nextnetwork devices. In an aspect, one or more network devices can choosethe likely next network devices. In the case of a digital fingerprintfor a particular user device 102, the particular user device 102 canchoose the likely next network devices.

In an aspect, the one or more likely next network devices chosen canreceive one or more keys for association. In an aspect, the one or morelikely next network devices chosen can pre-authenticate the user devicewith the one or more keys. For example, in a secure environment, the oneor more keys can help create a seamless transition between the networkdevices and a seamless experience for a user of the mobile device. In anaspect, the one or more likely next network devices chosen can receivecontent requested by the user device. For example, if a network iscongested and the mobile device is streaming video content, the one ormore likely next network devices chosen can receive some thresholdbuffer length, such as 30 seconds, of the streaming video content.Providing such content to the network devices can prevent networklatency from disrupting the streaming video content presented on themobile device. In FIG. 3, the network device 116 c can be identified asthe recipient the one or more keys 120 and/or the content 122 inresponse to the network device 116 c being chosen as the likely nextnetwork device.

FIG. 4 illustrates an exemplary method 400 executing on one or morenetwork devices 116, one or more user devices 102, and/or one or morecomputing devices 104 for implementing the systems and methods describedherein. At step 402, association information related to associationsbetween a plurality of network devices and a plurality of wirelessdevices can be received. In an aspect, a network device can be an accesspoint. In an aspect, a wireless device can be a user device 102, amobile device, a tablet, a smart phone, or the like. The associationinformation can comprise information related to associations (e.g.,connections, communications, etc. . . . ) made between the plurality ofwireless devices and the plurality of network devices. The associationinformation can comprise one or more identifiers for the plurality ofnetwork devices and/or the plurality of wireless devices. Theassociation information can further comprise a log (e.g., event history)indicative of each time one or more of the plurality of wireless devicesassociates (e.g., connects, communicates, etc. . . . ) with one or moreof the plurality of network devices. The association information can bereceived, for example, by logging each time a wireless device associateswith a network device. The association information can be logged andstored by one or more of the plurality of wireless devices, one or moreof the plurality of network devices, and/or one or more remote computingdevices.

At step 404, data related to one or more factors can be received. Afactor can comprise a circumstance (e.g., environmental information,calendar information, timing information), activity (e.g., applicationin use, type of movement), characteristic (e.g., user characteristic,device characteristic), and/or the like. Factors can be associated withan environment. Factors can be related to and/or associated with thepattern of association. Factors can be any information that providesinsight on a pattern of association. The one or more factors can beconsidered a general factor (e.g., applicable to the plurality ofwireless devices) and/or a specific factor (e.g., applicable to one ofthe plurality of wireless devices). General factors can comprise, forexample, a time of day, a day of the week, a time relative to an event(e.g., two minutes until halftime), a time relative to a schedule (e.g.,a transportation vehicle is scheduled to arrive in four minutes), typesof applications executing on the wireless devices, weather (e.g., a pathand/or a pace wireless devices take when it is raining and/or coldoutside), etc. Specific factors can comprise, for example, a time thewireless device has been associated with one or more network devices, acomprehensive history of one or more network devices with which thewireless device has associated, an abbreviated history of one or morenetwork devices with which the wireless device has associated (e.g., thecurrent trip, etc.), a history in relation to other factors (e.g., thepath the wireless device normally takes on Tuesday afternoons, etc.), alocation (e.g., the path the wireless device normally takes when in thiscity, etc.), types of applications executing on the wireless device(e.g., a home security application, a home automation application, andthe like), weather (e.g., a path and/or a pace the wireless device takeswhen it is raining and/or cold outside), etc.

In another aspect, the data related to one or more factors can bereceived through the use of one or more sensors, user input, sendingrequests to and/or receiving updates from local and/or remote services(such as, for example, a time/date service, a weather service, an eventscheduling service, etc.), and/or any other suitable method. In anaspect, the data can be received from the wireless device. For example,a clock on the wireless device can provide the time or a weatherapplication running on the wireless device can provide current weatherinformation.

At step 406, a likelihood that one of the plurality of wireless deviceswill associate with each of one or more of the plurality of networkdevices can be determined based on the association information.Determining the likelihood can comprise determining the likelihood thatthe one of the plurality of wireless devices will associate with one ormore of the plurality of network devices based on which of the pluralityof network devices the one of the plurality of wireless devices iscurrently associated with. In one aspect, the likelihood can bedetermined based on a number of times the one of the plurality ofwireless devices associates with a first network device afterassociating with a second network device. For example, out of athreshold number of occurrences (e.g., 10), if a given wireless devicethat is presently connected to network device A next connects to networkdevice B seven out of ten times, network device C two out of ten times,and network device D one out of ten times, then network device B canhave a likelihood of 70%, network device C can have a likelihood of 20%,and network device D can have a likelihood of 10%. More advancedstatistical methods, such as Bayesian statistics, can be used togenerate more advanced patterns of association.

At step 408, a pattern of association can be generated based on thedetermined likelihood(s). In an aspect, the pattern of association cancomprise information that indicates which of the plurality of networkdevices the one of the plurality of wireless devices is likely toconnect to and in what order. The pattern of association thus canidentify an order in which the one of the plurality of wireless devicesconnects to one or more of the plurality of network devices. In anaspect, the pattern of association can be associated with at least oneof the plurality of network devices and/or the one of the plurality ofwireless devices.

At step 410, the pattern of association can be associated with the oneor more factors. The pattern of association can also be associated withan identifier of the one of the plurality of wireless devices. Thepattern of association can also be associated with the one or morefactors based on which of the one or more factors was received/waspresent concurrently with the association information. In the aboveexample, if the threshold number of occurrences all occurred atapproximately the same time of day, then the resulting pattern ofassociation can be further associated with that time of day. The patternof association can be associated with an identifier of the plurality ofnetwork devices and/or the identifier of the one of the plurality ofwireless devices that resulted in and/or contributed to the generationof the pattern of association. In that way the pattern of associationcan be identified. The identifier can be any identifier, token,character, string, or the like, for differentiating one wireless devicefrom another wireless device. In a further aspect, the identifier canidentify a user or wireless device as belonging to a particular class ofusers or user devices. As a further example, the identifier can compriseinformation relating to the wireless device such as a manufacturer, amodel or type of device, and the like. The identifier can be, forexample, an internet protocol address, a network address, a media accesscontrol (MAC) address, and the like. In some aspect, the identifier canbe relied upon to establish a communication session (e.g., association)between the one of the plurality of wireless devices and one or more ofthe plurality of network devices.

Steps 402 through 410 can be repeated any number of times to create anynumber of patterns of association. For example, a variety of patterns ofassociation can be created that are specific to a particular factor, agroup of factors, a wireless device, a group of wireless devices, andcombinations thereof.

At step 412, a request to associate can be detected from the one of theplurality of wireless devices. The detection can be performed by the oneof the plurality of wireless devices, the plurality of network devices,a remote computing device (e.g., managed by a service provider and/or acontent provider), and combinations thereof. In an aspect, theidentifier of the one of the plurality of wireless devices can bereceived. In an aspect, when the one of the plurality of wirelessdevices associates with and/or attempts to associate with one or more ofthe plurality of network devices, the association (or attemptedassociation) can be dependent upon the exchange of the identifier forthe one of the plurality of wireless devices.

At step 414, a determination can be made if the pattern of associationis applicable to the detected request. The identifier can be compared tothe identifier associated with the pattern of association, if thecomparison results in a match then the pattern of association can beaccessed and utilized to transmit data according to the pattern ofassociation. In an aspect, the received identifier can be matched to theidentifier associated with the pattern of association. In this way, theone of the plurality of wireless devices, one or more of the pluralityof network devices, and/or a remote computing device can identify whichof possibly many stored patterns of association are applicable to theone of the plurality of wireless devices.

At step 416, a next network device that the one of the plurality ofwireless devices will associate with next can be determined based on thepattern of association if the pattern of association is applicable tothe detected request. In an aspect, the next network device can be amost likely next network device. In an aspect, the next network devicecan be one of a top x number of most likely next network devices. In anaspect, the next network device can be a network device with a highestprobability of being a likely next network device. In an aspect, thenext network device can have a probability of being a likely nextnetwork device exceeding a threshold percentage.

At step 418, authorization data can be transmitted to the next networkdevice to pre-authenticate the one of the plurality of wireless devicesat the next network device. In an aspect, the authorization data can beone or more keys.

FIG. 5 illustrates an exemplary method 500 executing on one or morenetwork devices 116, one or more user devices 102, and/or one or morecomputing devices 104 for implementing the systems and methods describedherein. At step 502, it can be determined that a wireless deviceassociated with a current network device is associated with a pattern ofassociation. In an aspect, the determination can be made by the wirelessdevice, the current network device, and/or a remote computing device(e.g., managed by a service provider and/or a content provider).Determining that the wireless device associated with the current networkdevice is associated with the pattern of association can comprisedetermining whether an identifier of the wireless device matches anidentifier associated with the pattern of association. For example, thedetermination can be made by receiving an indication in the form of amessaging protocol handshake acknowledgement. In an aspect, themessaging protocol handshake acknowledgment can comprise a standard802.11 probe request. In another aspect, the messaging protocolhandshake acknowledgment can comprise a standard 802.11 probe response.In a further aspect, the indication can be a message delivered via amessaging protocol. For example, the messaging protocol can compriseBluetooth Zigbee®, or any other messaging protocol.

At step 504, one or more factors can be determined. A factor cancomprise a circumstance (e.g., environmental information, calendarinformation, timing information), activity (e.g., application in use,type of movement), characteristic (e.g., user characteristic, devicecharacteristic), and/or the like. Factors can be associated with anenvironment. In an aspect, the one or more factors associated with theenvironment can be determined by receiving data from at least a sensor.In an aspect, the one or more factors associated with the environmentcan be determined by receiving data from at least a local service. In anaspect, the one or more factors associated with the environment can bedetermined by receiving data from at least a remote service. Factors canbe related to and/or associated with the pattern of association. Factorscan be any information that provides insight on the pattern ofassociation. The one or more factors can be considered a general factor(e.g., applicable to a plurality of wireless devices) and/or a specificfactor (e.g., applicable the wireless device). General factors cancomprise, for example, a time of day, a day of the week, a time relativeto an event (e.g., two minutes until halftime), a time relative to aschedule (e.g., a transportation vehicle is scheduled to arrive in fourminutes), types of applications executing on the plurality of wirelessdevices, weather (e.g., a path and/or a pace the plurality of wirelessdevices take when it is raining and/or cold outside), etc. Specificfactors can comprise, for example, a time the wireless device has beenassociated with one or more network devices, a comprehensive history ofone or more network devices with which the wireless device hasassociated, an abbreviated history of one or more network devices withwhich the wireless device has associated (e.g., the current trip, etc.),a history in relation to other factors (e.g., the path the wirelessdevice normally takes on Tuesday afternoons, etc.), a location (e.g.,the path the wireless device normally takes when in this city, etc.),types of applications executing on the wireless device (e.g., a homesecurity application, a home automation application, and the like),weather (e.g., a path and/or a pace the wireless device takes when it israining and/or cold outside), etc.

In an aspect, the determination of the one or more factors can beperformed by the wireless device, the current network device, a remotecomputing device, and combinations thereof. In another aspect, the oneor more factors can be determined through the use of one or moresensors, user input, sending requests to and/or receiving updates fromlocal and/or remote services (such as, for example, a time/date service,a weather service, an event scheduling service, etc.), and/or any othersuitable method. In an aspect, the information can be collected from thewireless device. For example, a factor such as a current time of day canbe determined by a clock that is either remote or local. By way offurther example, a factor such as current weather can be determined by aweather service.

At step 506, a determination can be made that the one or more factorsmatch one or more stored factors that are associated with the pattern ofassociation, if any. If the one or more factors matches the one or morestored factors that are associated with the pattern of association, thepattern of association can be selected for use. For example, a factorsuch as day of the week (e.g., Tuesday) can be determined and comparedwith stored factors to determine if any of the stored factors are amatching day of the week (e.g., Tuesday). In the event that the wirelessdevice is associated with more than one pattern of association, thecomparison of the one or more factors can be used to select the patternof association that best fits the present scenario. Comparison betweenthe one or more factors and the one or more stored factors can comprise,for example, string matching, date comparison, time comparison, and anyother technique for determining the differences and similarities betweendata.

At step 508, a next network device that the wireless device willassociate with next can be determined (e.g., predicted) based on thepattern of association. In an aspect, the pattern of association cancomprise one or more likelihoods that the wireless device will associatewith one or more of a plurality of network devices based on which of theplurality of network devices the wireless device is currently associatedwith. In one aspect, the likelihood can be determined based on a numberof times a wireless device associates a first network device afterassociating with a second network device. The likelihood can vary amongdifferent patterns of association based on the presence and/or absencethe one or more factors. For example, out of a threshold number ofoccurrences (e.g., 10), if a given wireless device that is presentlyconnected to network device A next connects to network device B sevenout of ten times, network device C two out of ten times, and networkdevice D one out of ten times, then network device B can have alikelihood of 70%, network device C can have a likelihood of 20%, andnetwork device D can have a likelihood of 10%. More advanced statisticalmethods, such as Bayesian statistics, can be used to generate moreadvanced patterns of association.

In an aspect, the next network device can be the network device having ahighest likelihood in the pattern of association. In another aspect, apredetermined number of network devices can be determined to be the nextnetwork device. For example, the network devices having the top threehighest likelihoods can be identified collectively as the next networkdevice.

At step 510, authorization data can be transmitted to the next networkdevice to pre-authenticate the wireless device at the next networkdevice. If more than one network device has been determined to be thenext network device, the data can be transmitted to each network deviceidentified as the next network device. In an aspect, the transmitteddata can comprise authorization data, such as one or more keys. In anaspect, the one or more keys can be used to pre-authenticate thewireless device at the next network device. In an aspect, the data cancomprise content, such as static content and/or streaming content. Inthe case of streaming content, content currently streaming to thewireless device can be prepositioned at (e.g., provided in advance ofthe wireless device being in range) the next network device (e.g., thestreaming content can be multicast to the current network device and thenext network device). An estimation can be made about the time ofarrival at the next network device. An approximation of where in thecontent the wireless device will be upon arrival at the next networkdevice can be made based on the estimation. In an aspect, a portion ofthe content can be transmitted to the next network device and held instorage based on the approximation. For example, if it is estimated thatthe wireless device will be at the next network device in 2 minutes, andthe wireless device is currently 17 minutes into the content, then theapproximation can be that the wireless device will be at 19 minutes whenthe wireless device arrives in range of the next network device. A30-second portion of the content starting at 18 minutes and 45 secondsinto the content and ending at 19 minutes and 15 seconds into thecontent can be transmitted to a buffer of the next network device sothat the content is available and ready for transmission to the wirelessdevice.

Optionally, a determination can be made that the wireless device did notassociate with the next network device and the pattern of associationcan be altered accordingly. In an aspect, a confidence level in thepattern of association can be reduced. If the confidence level is belowa threshold then the pattern of association can be discarded or madedormant. In an aspect, if the pattern of association is specific for aparticular wireless device, then deviations from expected behavior canalter the pattern of association or cause multiple patterns to becreated. The pattern of association can be associated with a dormantstate if a deviation from expected behavior occurs. While the pattern ofassociation has a dormant state, the pattern of association can be(e.g., at least temporarily) ignored or given decreased weight. Thepattern of association can be returned to an active state upon theoccurrence of compliance with the pattern of association (e.g., thewireless device associating with network devices in accordance with thepattern of association). In an aspect, a threshold number of occurrencesof compliance can be required before returning the pattern ofassociation to an active state. For example, a user who normally worksfrom an office building may be working from a library for several weeks.The user may normally park in a subway parking lot and take a trainnorth to the office building. However, while the user is working fromthe library, the user may park in the subway parking lot and take atrain south to the library. After a threshold number (e.g., two) ofinstances of noncompliance with the pattern of association, the patternof association can go dormant. In an aspect, the pattern of associationcan be revived upon an instance of compliance with the pattern ofassociation. In an aspect, if a pattern of noncompliance with thepattern of association is suitable for a formation of another pattern,then a second pattern can be created. For example, if the user goes tohis office building every weekday except Tuesdays, on which he goes tothe library, then the pattern of association can be edited to excludeTuesdays from consideration and a second pattern specifically forTuesdays can be created.

In an aspect, the pattern of association can be discarded after athreshold number of noncompliance instances. For example, if the userthat normally parks at the subway parking lot and takes the train north,gets a new job requiring the user to take the train south, then thesystem can discard the pattern of association after a threshold number(e.g., thirty) of noncompliant instances. In an aspect, a newly createdpattern of association can replace the discarded pattern. Optionally,content being consumed by the wireless device can be transmitted to thecurrent network device and the next network device.

FIG. 6 illustrates an exemplary method 600 executing on one or morenetwork devices 116, user devices 102, and/or one or more computingdevices 104 for implementing the systems and methods described herein.At step 602, information regarding associations between a plurality ofnetwork devices and a wireless device and one or more factors can becollected. In an aspect, the one or more factors can be associated withan environment. In an aspect, a network device can be an access point.In an aspect, a wireless device can be a user device 102, a tablet, asmart phone, or the like.

The collected information can comprise association information relatedto associations between the plurality of network devices and theplurality of wireless devices. The association information can compriseinformation related to associations (e.g., connections, communications,etc. . . . ) made between the plurality of wireless devices and theplurality of network devices. The association information can compriseone or more identifiers for the plurality of network devices and/or theplurality of wireless devices. The association information can furthercomprise a log indicative of each time one or more of the plurality ofwireless devices associates with one or more of the plurality of networkdevices. The association information can be collected, for example, bylogging each time a wireless device associates with a network device.The association information can be logged and stored by one or more ofthe plurality of wireless devices, one or more of the plurality ofnetwork devices, and/or one or more remote computing devices.

The collected information can comprise data related to one or morefactors. A factor can comprise a circumstance (e.g., environmentalinformation, calendar information, timing information), activity (e.g.,application in use, type of movement), characteristic (e.g., usercharacteristic, device characteristic), and/or the like. Factors can berelated to and/or associated with the pattern of association. Factorscan be any information that provides insight on the pattern ofassociation. The one or more factors can be considered a general factor(e.g., applicable to the plurality of wireless devices) and/or aspecific factor (e.g., applicable to one of the plurality of wirelessdevices). General factors can comprise, for example, a time of day, aday of the week, a time relative to an event (e.g., two minutes untilhalftime), a time relative to a schedule (e.g., a transportation vehicleis scheduled to arrive in four minutes), types of applications executingon the plurality of wireless devices, weather (e.g., a path and/or apace the plurality of wireless devices take when it is raining and/orcold outside), etc. Specific factors can comprise, for example, a timethe wireless device has been associated with one or more networkdevices, a comprehensive history of one or more network devices withwhich the wireless device has associated, an abbreviated history of oneor more network devices with which the wireless device has associated(e.g., the current trip, etc.), a history in relation to other factors(e.g., the path the wireless device normally takes on Tuesdayafternoons, etc.), a location (e.g., the path the wireless devicenormally takes when in this city, etc.), types of applications executingon the wireless device (e.g., a home security application, a homeautomation application, and the like), weather (e.g., a path and/or apace the wireless device takes when it is raining and/or cold outside),etc.

In another aspect, the data related to one or more factors can becollected through the use of one or more sensors, user input, sendingrequests to and/or receiving updates from local and/or remote services(such as, for example, a time/date service, a weather service, an eventscheduling service, etc.), and/or any other suitable method. In anaspect, the information can be collected from the wireless device. Forexample, a clock on the wireless device can provide the time or aweather application running on the wireless device can provide currentweather information.

At step 604, the collected information can be generated based on apattern of association associated with at least one of the plurality ofnetwork devices. In an aspect, the pattern of association can be createdas described in FIG. 4. The pattern of association can compriseinformation that indicates to which of the plurality of network devicesthat the wireless device is likely to connect. The pattern ofassociation thus can identify an order in which the plurality ofwireless devices connects to one or more of the plurality of networkdevices.

At step 606, an indication that the wireless device is currentlyassociated with the at least one of the plurality of network devices canbe received. In an aspect, the indication can be received from thewireless device and/or the at least one of the plurality of networkdevices. In a further aspect, the indication can be a messaging protocolhandshake acknowledgement. For example, the messaging protocol handshakeacknowledgment can comprise a standard 802.11 probe request. In anotherexample, the messaging protocol handshake acknowledgment can comprise astandard 802.11 probe response. In an aspect, the indication can bereceived from a network device. The network device can be a deviceconfigured to provide access to a network using a Wi-Fi protocol, suchas an access point. In a further aspect, the indication can be a messagedelivered via a messaging protocol. For example, the messaging protocolcan comprise Bluetooth Zigbee®, or any other messaging protocol.

In step 608, a determination can be made that the one or more factorsare met (e.g., present). If the one or more factors are met, the patternof association can be selected for use. Determining that the one or morefactors are met can comprise, for example, string matching, datecomparison, time comparison, and any other technique for determining thedifferences and similarities between data.

Determinations for factors can be made through the interpretation ofinformation from sensors, received signals, user input, and/or any othersuitable method. For example, if the factor is that the day of the weekis Tuesday, a determination can be made that the day of the week isTuesday. In another example, if the factor is occurrence of a sportingevent, an event schedule can be accessed to determine if a sportingevent is currently underway. In another example, if the factor isarrival/departure of a transportation vehicle (e.g., a bus, a train,etc.), a schedule can be accessed and a determination can be made of thetime of arrival/departure and/or location of the transportation vehicle.In an aspect, factors can be determined locally, for example by aprogram configured to keep time or the schedule. In an aspect, factorscan be determined from received signals, such as, for example, signalscomprising the time, schedule, and/or location. In an aspect, thesignals can be received from the wireless device, a service, a computingdevice, another network device, or any other signal source. In anaspect, factors can comprise environmental changes, such as a change inseason, a change in weather, etc. For example, a pace a wireless devicetakes can increase when it is raining and/or cold outside. In anotherexample, a pace a wireless device takes can decrease when it is warmoutside. In another example, a path a wireless device takes can changewhen it is raining and/or cold outside. In another example, a path awireless device takes can change when it is warm outside.

In step 610, a next network device that the wireless device willassociate with can be determined based on the pattern of association. Inan aspect, the pattern of association can comprise one or morelikelihoods that the wireless device will associate with one or more ofthe plurality of network devices based on which of the plurality ofnetwork devices the wireless device is currently associated with. In anaspect, the next network device can be the network device having ahighest likelihood in the pattern of association. In another aspect, apredetermined number of network devices can be determined to be the nextnetwork device. For example, the network devices having the top threehighest likelihoods can be identified collectively as the next networkdevice.

At step 612, authorization data for the wireless device can betransmitted to the determined another of the plurality of networkdevices to pre-authenticate the wireless device at the determinedanother of the plurality of network devices. If more than one networkdevice has been determined to be the next network device, the data canbe transmitted to each network device identified as the next networkdevice. In an aspect, the transmitted data can comprise authorizationdata, such as one or more keys. In an aspect, the one or more keys canbe used to pre-authenticate the wireless device at the next networkdevice. In an aspect, the data can comprise content, such as staticcontent and/or streaming content. In the case of streaming content,content currently streaming to the wireless device can be prepositionedat the next network device (e.g., the streaming content can be multicastto the current network device and the next network device). Anestimation can be made about the time of arrival at the next networkdevice. An approximation of where in the content the wireless devicewill be upon arrival at the next network device can be made based on theestimation. In an aspect, a portion of the content can be transmitted tothe next network device and held in storage based on the approximation.For example, if it is estimated that the wireless device will be at thenext network device in 2 minutes, and the wireless device is currently17 minutes into the content, then the approximation can be that thewireless device will be at 19 minutes when the wireless device arrivesin range of the next network device. A 30-second portion of the contentstarting at 18 minutes and 45 seconds into the content and ending at 19minutes and 15 seconds into the content can be transmitted to a bufferof the next network device so that the content is available and readyfor transmission to the wireless device. Optionally, content beingconsumed by the wireless device can be transmitted to the at least oneof the plurality of network devices and the determined another of theplurality of network devices.

In an exemplary aspect, the methods and systems can be implemented on acomputer 701 as illustrated in FIG. 7 and described below. By way ofexample, computing device 104, user device 102, and/or one or more ofthe network devices 116 a,b,c,d of FIG. 1 can be a computer 701 asillustrated in FIG. 7. Similarly, the methods and systems disclosed canutilize one or more computers to perform one or more functions in one ormore locations. FIG. 7 is a block diagram illustrating an exemplaryoperating environment 700 for performing the disclosed methods. Thisexemplary operating environment 700 is only an example of an operatingenvironment and is not intended to suggest any limitation as to thescope of use or functionality of operating environment architecture.Neither should the operating environment 700 be interpreted as havingany dependency or requirement relating to any one or combination ofcomponents illustrated in the exemplary operating environment 700.

The present methods and systems can be operational with numerous othergeneral purpose or special purpose computing system environments orconfigurations. Examples of well-known computing systems, environments,and/or configurations that can be suitable for use with the systems andmethods comprise, but are not limited to, personal computers, servercomputers, laptop devices, and multiprocessor systems. Additionalexamples comprise set top boxes, programmable consumer electronics,network PCs, minicomputers, mainframe computers, distributed computingenvironments that comprise any of the above systems or devices, and thelike.

The processing of the disclosed methods and systems can be performed bysoftware components. The disclosed systems and methods can be describedin the general context of computer-executable instructions, such asprogram modules, being executed by one or more computers or otherdevices. Generally, program modules comprise computer code, routines,programs, objects, components, data structures, and/or the like thatperform particular tasks or implement particular abstract data types.The disclosed methods can also be practiced in grid-based anddistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed computing environment, program modules can be located inlocal and/or remote computer storage media such as memory storagedevices.

Further, one skilled in the art will appreciate that the systems andmethods disclosed herein can be implemented via a general-purposecomputing device in the form of a computer 701. The computer 701 cancomprise one or more components, such as one or more processors 703, asystem memory 712, and a bus 713 that couples various components of thecomputer 701 comprising the one or more processors 703 to the systemmemory 712. The system can utilize parallel computing.

The bus 713 can comprise one or more of several possible types of busstructures, such as a memory bus, memory controller, a peripheral bus,an accelerated graphics port, or local bus using any of a variety of busarchitectures. By way of example, such architectures can comprise anIndustry Standard Architecture (ISA) bus, a Micro Channel Architecture(MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics StandardsAssociation (VESA) local bus, an Accelerated Graphics Port (AGP) bus,and a Peripheral Component Interconnects (PCI), a PCI-Express bus, aPersonal Computer Memory Card Industry Association (PCMCIA), UniversalSerial Bus (USB) and the like. The bus 713, and all buses specified inthis description can also be implemented over a wired or wirelessnetwork connection and one or more of the components of the computer701, such as the one or more processors 703, a mass storage device 704,an operating system 705, predictive roaming software 706, predictiveroaming data 707, a network adapter 708, the system memory 712, anInput/Output Interface 710, a display adapter 709, a display device 711,and a human machine interface 702, can be contained within one or moreremote computing devices 714 a,b,c at physically separate locations,connected through buses of this form, in effect implementing a fullydistributed system.

The computer 701 typically comprises a variety of computer readablemedia.

Exemplary readable media can be any available media that is accessibleby the computer 701 and comprises, for example and not meant to belimiting, both volatile and non-volatile media, removable andnon-removable media. The system memory 712 can comprise computerreadable media in the form of volatile memory, such as random accessmemory (RAM), and/or non-volatile memory, such as read only memory(ROM). The system memory 712 typically can comprise data such as thepredictive roaming data 707 and/or program modules such as the operatingsystem 705 and the predictive roaming software 706 that are accessibleto and/or are operated on by the one or more processors 703.

In another aspect, the computer 701 can also comprise otherremovable/non-removable, volatile/non-volatile computer storage media.The mass storage device 704 can provide non-volatile storage of computercode, computer readable instructions, data structures, program modules,and other data for the computer 701. For example, the mass storagedevice 704 can be a hard disk, a removable magnetic disk, a removableoptical disk, magnetic cassettes or other magnetic storage devices,flash memory cards, CD-ROM, digital versatile disks (DVD) or otheroptical storage, random access memories (RAM), read only memories (ROM),electrically erasable programmable read-only memory (EEPROM), and thelike.

Optionally, any number of program modules can be stored on the massstorage device 704, such as, by way of example, the operating system 705and the predictive roaming software 706. One or more of the operatingsystem 705 and the predictive roaming software 706 (or some combinationthereof) can comprise elements of the programming and the predictiveroaming software 706. The predictive roaming data 707 can also be storedon the mass storage device 704. The predictive roaming data 707 can bestored in any of one or more databases known in the art. Examples ofsuch databases comprise, DB2®, Microsoft® Access, Microsoft® SQL Server,Oracle®, mySQL, PostgreSQL, and the like. The databases can becentralized or distributed across multiple locations within the network715.

In another aspect, the user can enter commands and information into thecomputer 701 via an input device (not shown). Examples of such inputdevices comprise, but are not limited to, a keyboard, pointing device(e.g., a computer mouse, remote control), a microphone, a joystick, ascanner, tactile input devices such as gloves, and other body coverings,motion sensor, and the like These and other input devices can beconnected to the one or more processors 703 via the human machineinterface 702 that is coupled to the bus 713, but can be connected byother interface and bus structures, such as a parallel port, game port,an IEEE 1394 Port (also known as a Firewire port), a serial port, anetwork adapter 708, and/or a universal serial bus (USB).

In yet another aspect, the display device 711 can also be connected tothe bus 713 via an interface, such as the display adapter 709. It iscontemplated that the computer 701 can have more than one displayadapter 709 and the computer 701 can have more than one display device711. For example, the display device 711 can be a monitor, an LCD(Liquid Crystal Display), light emitting diode (LED) display,television, smart lens, smart glass, and/or a projector. In addition tothe display device 711, other output peripheral devices can comprisecomponents such as speakers (not shown) and a printer (not shown) whichcan be connected to the computer 701 via an Input/Output Interface 710.Any step and/or result of the methods can be output in any form to anoutput device. Such output can be any form of visual representation,comprising, but not limited to, textual, graphical, animation, audio,tactile, and the like. The display device 711 and the computer 701 canbe part of one device, or separate devices.

The computer 701 can operate in a networked environment using logicalconnections to one or more remote computing devices 714 a,b,c. By way ofexample, a remote computing device 714 a,b,c can be a personal computer,computing station (e.g., workstation), portable computer (e.g., laptop,mobile phone, tablet device), smart device (e.g., smartphone, smartwatch, activity tracker, smart apparel, smart accessory), securityand/or monitoring device, a server, a router, a network computer, a peerdevice, edge device or other common network node, and so on. Logicalconnections between the computer 701 and a remote computing device 714a,b,c can be made via a network 715, such as a local area network (LAN)and/or a general wide area network (WAN). Such network connections canbe through the network adapter 708. The network adapter 708 can beimplemented in both wired and wireless environments. Such networkingenvironments are conventional and commonplace in dwellings, offices,enterprise-wide computer networks, intranets, and the Internet.

For purposes of illustration, application programs and other executableprogram components such as the operating system 705 are illustratedherein as discrete blocks, although it is recognized that such programsand components can reside at various times in different storagecomponents of the computing device 701, and are executed by the one ormore processors 703 of the computer 701. An implementation of thepredictive roaming software 706 can be stored on or transmitted acrosssome form of computer readable media. Any of the disclosed methods canbe performed by computer readable instructions embodied on computerreadable media. Computer readable media can be any available media thatcan be accessed by a computer. By way of example and not meant to belimiting, computer readable media can comprise “computer storage media”and “communications media.” “Computer storage media” can comprisevolatile and non-volatile, removable and non-removable media implementedin any methods or technology for storage of information such as computerreadable instructions, data structures, program modules, or other data.Exemplary computer storage media can comprise RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich can be used to store the desired information and which can beaccessed by a computer.

The methods and systems can employ artificial intelligence (AI)techniques such as machine learning and iterative learning. Examples ofsuch techniques comprise, but are not limited to, expert systems, casebased reasoning, Bayesian networks, behavior based AI, neural networks,fuzzy systems, evolutionary computation (e.g. genetic algorithms), swarmintelligence (e.g. ant algorithms), and hybrid intelligent systems (e.g.Expert inference rules generated through a neural network or productionrules from statistical learning).

While the methods and systems have been described in connection withpreferred embodiments and specific examples, it is not intended that thescope be limited to the particular embodiments set forth, as theembodiments herein are intended in all respects to be illustrativerather than restrictive.

Unless otherwise expressly stated, it is in no way intended that anymethod set forth herein be construed as requiring that its steps beperformed in a specific order. Accordingly, where a method claim doesnot actually recite an order to be followed by its steps or it is nototherwise specifically stated in the claims or descriptions that thesteps are to be limited to a specific order, it is in no way intendedthat an order be inferred, in any respect. This holds for any possiblenon-express basis for interpretation, such as: matters of logic withrespect to arrangement of steps or operational flow; plain meaningderived from grammatical organization or punctuation; the number or typeof embodiments described in the specification.

It will be apparent to those skilled in the art that variousmodifications and variations can be made without departing from thescope or spirit. Other embodiments will be apparent to those skilled inthe art from consideration of the specification and practice disclosedherein. It is intended that the specification and examples be consideredas exemplary only, with a true scope and spirit being indicated by thefollowing claims.

1. A method comprising: determining, based on at least one associationof a wireless device with one or more network devices of a plurality ofnetwork devices, association information; determining an environmentalfactor associated with the wireless device; determining, based on theassociation information and the environmental factor, a first networkdevice of the plurality of network devices; and sending, to the firstnetwork device, authorization data configured to authenticate thewireless device at the first network device.
 2. The method of claim 1,further comprising determining a timing factor, wherein determining thefirst network device of the plurality of network devices is based on theassociation information, the environmental factor and the timing factor.3. The method of claim 2, wherein the timing factor comprises one ormore of a time of day, a day of a week, calendar information, timinginformation, a time relative to an event, a time relative to a schedule,and a time the wireless device has previously been associated with thefirst network device.
 4. The method of claim 1, wherein determining thefirst network device of the plurality of network devices comprises:ranking, based on a respective percentage of total associations of thewireless device within a time period, each network device of theplurality of network devices; and determining that the respectivepercentage of the first network device satisfies a threshold.
 5. Themethod of claim 1, wherein determining the environmental factorassociated with the wireless device comprises determining, based on dataassociated with one or more of a sensor or a service, the environmentalfactor associated with an environment, wherein the environmental factorassociated with the wireless device comprises information related to oneor more of, a physical environment of the wireless device, weather, alocation of the wireless device, a digital environment of the wirelessdevice, and environmental information.
 6. The method of claim 1, furthercomprising causing at least a portion of content being sent to thewireless device to be sent to the first network device via one or moreof a unicast transmission or a multicast transmission.
 7. The method ofclaim 1, further comprising: determining, based on the environmentalfactor, an expected path of the wireless device; and determining, basedon the expected path of the wireless device, a portion of the pluralityof network devices, wherein the portion of the plurality of networkdevices comprises the first network device.
 8. The method of claim 1,further comprising determining that the wireless device is currentlyassociated with a second network device of the plurality of networkdevices, wherein determining the first network device is further basedon the second network device.
 9. A method comprising: determining, basedon at least one association of a wireless device with one or morenetwork devices of a plurality of network devices, associationinformation; determining a timing factor associated with the wirelessdevice; determining, based on the association information and the timingfactor, a first network device of the plurality of network devices; andsending, to the first network device, authorization data configured toauthenticate the wireless device at the first network device.
 10. Themethod of claim 9, wherein the timing factor comprises one or more of atime of day, a day of a week, calendar information, timing information,a time relative to an event, a time relative to a schedule, and a timethe wireless device has previously been associated with the firstnetwork device.
 11. The method of claim 9, further comprisingdetermining one or more of an environmental factor, an activity factor,and a characteristic factor associated with the wireless device, whereindetermining the first network device of the plurality of network devicesis based on the association information, the timing factor, and one ormore of the environmental factor, the activity factor, and thecharacteristic factor associated with the wireless device.
 12. Themethod of claim 11, wherein the activity factor comprises one or more ofan application currently operating on the wireless device and a type ofmovement of the wireless device and wherein the characteristic factorcomprises one or more of a characteristic of a user of the wirelessdevice and a characteristic of the wireless device.
 13. The method ofclaim 9, wherein determining the first network device of the pluralityof network devices comprises: ranking, based on a respective percentageof total associations of the wireless device within a time period, eachnetwork device of the plurality of network devices; and determining thatthe respective percentage of the first network device satisfies athreshold.
 14. The method of claim 9, wherein the authorization datacomprises one or more temporal keys, wherein the authorization data willfail to authenticate the wireless device at the first network devicewhen the one or more temporal keys expire.
 15. The method of claim 9,further comprising: determining, based on the timing factor, an expectedpath of the wireless device; and determining, based on the expected pathof the wireless device, a portion of the plurality of network devices,wherein the portion of the plurality of network devices comprises thefirst network device.
 16. An apparatus comprising: one or moreprocessors; and memory storing processor executable instructions that,when executed by the one or more processors, cause the apparatus to:determine, based on at least one association of a wireless device withone or more network devices of a plurality of network devices,association information; determine one or more of a timing factor and anenvironmental factor associated with the wireless device; determine,based on the association information and the one or more of the timingfactor and the environmental factor, a first network device of theplurality of network devices; and send, to the first network device,authorization data configured to authenticate the wireless device at thefirst network device.
 17. The apparatus of claim 16, wherein theprocessor executable instructions, when executed by the one or moreprocessors, further cause the apparatus to determine that the wirelessdevice is currently associated with a second network device of theplurality of network devices, and wherein the processor executableinstructions that, when executed by the one or more processors,determine the first network device of the plurality of network devicesfurther cause the apparatus to determine, based on the associationinformation, the one or more of the timing factor and the environmentalfactor, and the second network device, the first network device of theplurality of network devices.
 18. The apparatus of claim 16, the timingfactor comprises one or more of a time of day, a day of a week, calendarinformation, timing information, a time relative to an event, a timerelative to a schedule, and a time the wireless device has previouslybeen associated with the first network device.
 19. The apparatus ofclaim 16, wherein the environmental factor associated with the wirelessdevice comprises information related to one or more of, a physicalenvironment of the wireless device, weather, a location of the wirelessdevice, a digital environment of the wireless device, and environmentalinformation.
 20. The apparatus of claim 16, wherein the processorexecutable instructions, when executed by the one or more processors,further cause the apparatus to: rank, based on a respective percentageof total associations of the wireless device within a time period, eachnetwork device of the plurality of network devices; and determine thatthe respective percentage of the first network device satisfies athreshold.